Multi-modal planning is promising for autonomous driving by representing multiple plausible behaviors in ambiguous and long-tail scenarios. Existing methods mainly focus on improving trajectory multi-modality, enhancing trajectory representations, or reshaping the candidate distribution. Nevertheless, we identify a pronounced generation-evaluation asymmetry in multi-modal planning: despite strong oracle performance, existing planners often fail to reliably select the best available candidate, leaving substantial planning potential unrealized. To address this challenge, we propose iDriveVLA, a multi-modal planning framework that improves the candidate trajectory space while enabling more reliable and context-aware trajectory evaluation. Specifically, iDriveVLA introduces a unified trajectory evaluator comprising a Safety-aware Scorer for quality and risk estimation, together with a VLM-guided Modulator for scene-adaptive criterion weighting. We further develop an oracle-aligned progressive training strategy consisting of candidate imitation pretraining, candidate space refinement, and semantic ranking alignment. On the public NAVSIM v1 leaderboard, iDriveVLA achieves a new state-of-the-art performance of 94.95 PDMS, surpassing the human-expert reference.
Figures & tables
Figure 1: Core idea. (a) Multiple plausible trajectories may coexist in challenging scenes, making reliable trajectory selection critical. (b) Increasing trajectory diversity improves oracle performance but also introduces more competing behaviors for the evaluator, revealing a generation–evaluation asymmetry in multi-modal planning. (c) This asymmetry leaves substantial oracle potential untapped in the strong multi-modal planner DrivoR ( Kirby et al., 2026 ) . In contrast, iDriveVLA improves trajectory evaluation, substantially narrows the oracle gap, and surpasses the human-expert reference on NAVSIM v1 ( Dauner et al., 2024 ) .
Figure 2: PDMS distributions of the selected and oracle candidates from the base planner. The oracle distribution is strongly concentrated near high PDMS, whereas the selected candidates exhibit a substantially heavier low-score tail.
Figure 3: Overview of the iDriveVLA framework. The candidate generator produces multiple trajectory hypotheses from multi-view observations and the ego state. Our evaluator consists of a safety-aware Scorer and a VLM-guided Modulator. The Scorer predicts criterion-wise planning scores and safety risks, while the Modulator generates scene-adaptive criterion weights. Unsafe candidates are filtered before score aggregation, and the highest-scoring trajectory is selected. The framework is progressively optimized through three training stages. Flame and snowflake icons denote trainable and frozen modules, respectively.
Table 2: Closed-loop evaluation on NAVSIM v2. All results are computed using the original NAVSIM v2 evaluation code without the human-behavior filtering fix.
Components
Planning Metrics
Δ PDMS
Safety Gate
Refinement
Modulator
NC ↑
DAC ↑
TTC ↑
Comf. ↑
EP ↑
PDMS ↑
Base Planner
99.0
98.9
96.7
100.0
90.0
93.7
–
✓
99.1
98.9
96.7
100.0
91.1
93.9
+0.2
✓
✓
99.0
99.0
96.6
100.0
91.3
94.2
+0.5
✓
✓
✓
99.1
99.1
96.6
100.0
91.6
94.4
+0.7
Table 3: Ablation study of the proposed components on NAVSIM v1. Safety Gate denotes the Safety-aware Scorer, Refinement denotes candidate space refinement fine-tuning, and Modulator denotes the VLM-guided Modulator. All variants are trained exclusively on NAVSIM data.
Figure 4: Comparison between DrivoR and iDriveVLA. Red and green curves denote the DrivoR and iDriveVLA trajectories, respectively. The paired camera and bird’s-eye-view panels show how the predicted paths differ under varied road geometry and surrounding traffic.
Figure 5: Qualitative comparison of trajectory selection without and with the VLM-guided Modulator. Red and green curves denote the selected trajectories without and with the Modulator, respectively. The Modulator changes the ranking of existing candidates according to the surrounding road structure and traffic context.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Configuration
Value
Candidate number K
64
Future planning steps T
8
Planning time interval
0.5 s
Visual registers per view
16
Trajectory decoder layers
4
Scorer layers
4
Appendix
Table 4: Model and training configurations.
Module
Time (ms)
Parameter memory (MiB)
Vision Encoder
97.73
90.53
Action Decoder
1.25
41.74
Scorer
3.40
7.15
Safety Head
0.20
2.02
Modulator
194.62
3999.51
Total
297.20
4140.95
Appendix
Table 5: Computational and memory overhead of the iDriveVLA inference pipeline.
Figure 6: Representative catastrophic selection failures of DrivoR. Yellow curves denote the candidate trajectories, while red and green curves denote the selected and oracle trajectories, respectively. Despite the presence of a safe oracle trajectory, the evaluator selects an unsafe candidate with zero PDMS.
Figure 7: Qualitative comparison between the trajectory selected by iDriveVLA and the oracle trajectory. The selected trajectories closely follow or overlap with the oracle trajectories, demonstrating reliable identification of high-quality candidates.
Safe and efficient trajectory planning is essential in autonomous driving. However, existing end-to-end approaches often fall short in both computational efficiency and safety guarantees. Methods based on imitation learning suffer from causal confusion, while rule-based scoring approaches often incur heavy computational overhead and suffer from objective misalignment. Additionally, preference-based methods rely on strict pairwise annotations, limiting data utilization. To overcome these limitations, we propose EMPlan, an efficient multi-modal trajectory planning method powered by reward-guided fine-tuning. We design a hybrid architecture that combines sparse anchors with an offset refinement module for efficient multi-modal trajectory prediction. Sparse anchors provide coarse trajectory candidates with low latency, which are subsequently refined by the offset module for higher prediction accuracy. To enhance safety without incurring additional inference costs, we adopt a two-stage training paradigm consisting of pretraining and reward-guided fine-tuning. During fine-tuning, we leverage rule-based reward signals and unpaired preference supervision to refine the pretrained policy toward safer trajectory selection. We evaluate EMPlan on the non-reactive NAVSIM benchmark, where it strikes a favorable balance between planning accuracy and efficiency, demonstrating superior performance under real-time constraints.
End-to-end multi-modal planning has been widely adopted to model the uncertainty of driving behavior, typically by scoring candidate trajectories and selecting the optimal one. Existing approaches generally fall into two categories: scoring a large static trajectory vocabulary, or scoring a small set of dynamically generated proposals. While static vocabularies often suffer from coarse discretization of the action space, dynamic proposals provide finer-grained precision and have shown stronger empirical performance on existing benchmarks. However, it remains unclear whether dynamic generation is fundamentally necessary, or whether static vocabularies can already achieve comparable performance when they are sufficiently dense to cover the action space. In this work, we start with a systematic scaling study of Hydra-MDP, a representative scoring-based method, revealing that performance consistently improves as trajectory anchors become denser, without exhibiting saturation before computational constraints are reached. Motivated by this observation, we propose SparseDriveV2 to push the performance boundary of scoring-based planning through two complementary innovations: (1) a scalable vocabulary representation with a factorized structure that decomposes trajectories into geometric paths and velocity profiles, enabling combinatorial coverage of the action space, and (2) a scalable scoring strategy with coarse factorized scoring over paths and velocity profiles followed by fine-grained scoring on a small set of composed trajectories. By combining these two techniques, SparseDriveV2 achieves 92.0 PDMS and 90.1 EPDMS on NAVSIM, with 89.15 Driving Score and 70.00 Success Rate on Bench2Drive with a lightweight ResNet-34 as backbone. Code and model are released at https://github.com/swc-17/SparseDriveV2.
Wenchao Sun, Xuewu Lin, Keyu Chen +4
Tsinghua University, Beijing, China · Horizon Continental Technology, Beijing, China · Horizon, Beijing, China
While multimodal large language models (MLLMs) provide advanced reasoning for autonomous driving, translating their discrete semantic knowledge into continuous trajectories remains a fundamental challenge. Existing methods often rely on unimodal planning heads that inherently limit their ability to represent multimodal driving behavior. Furthermore, most generative approaches frequently condition on one-hot encoded actions, discarding the nuanced navigational uncertainty critical for complex scenarios. To resolve these limitations, we introduce LAD-Drive, a generative framework that structurally disentangles high-level intention from low-level spatial planning. LAD-Drive employs an action decoder to infer a probabilistic meta-action distribution, establishing an explicit belief state that preserves the nuanced intent typically lost by one-hot encodings. This distribution, fused with the vehicle's kinematic state, conditions an action-aware diffusion decoder that utilizes a truncated denoising process to refine learned motion anchors into safe, kinematically feasible trajectories. Extensive evaluations on the LangAuto benchmark demonstrate that LAD-Drive achieves state-of-the-art results, outperforming competitive baselines by up to 59% in Driving Score while significantly reducing route deviations and collisions. We will publicly release the code and models on https://github.com/iis-esslingen/lad-drive.
Fabian Schmidt, Karol Fedurko, Markus Enzweiler +1
Institute for Intelligent Systems, Esslingen University of Applied Sciences, Germany. · Department of Computer Science, University of Freiburg, Germany.